DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Pre-trained Language Models

Pre-trained language models (PLMs) that achieve success in applications are susceptible to adversarial attack methods that are capable of generating adversarial examples with minor perturbations. Although recent attack methods can achieve a relatively high attack success rate (ASR), our observation shows that the generated adversarial examples have a different data distribution compared with the original examples. Specifically, these adversarial examples exhibit lower confidence levels and higher distance to the training data distribution. As a result, they are easy to detect using very simple detection methods, diminishing the actual effectiveness of these attack methods. To solve this problem, we propose a Distribution-Aware LoRA-based Adversarial Attack (DALA) method, which considers the distribution shift of adversarial examples to improve attack effectiveness under detection methods. We further design a new evaluation metric NASR combining ASR and detection for the attack task. We conduct experiments on four widely-used datasets and validate the attack effectiveness on ASR and NASR of the adversarial examples generated by DALA on the BERT-base model and the black-box LLaMA2-7b model.Comment: First two authors contribute equall

Isometric 3D Adversarial Examples in the Physical World

3D deep learning models are shown to be as vulnerable to adversarial examples as 2D models. However, existing attack methods are still far from stealthy and suffer from severe performance degradation in the physical world. Although 3D data is highly structured, it is difficult to bound the perturbations with simple metrics in the Euclidean space. In this paper, we propose a novel $\epsilon$-isometric ($\epsilon$-ISO) attack to generate natural and robust 3D adversarial examples in the physical world by considering the geometric properties of 3D objects and the invariance to physical transformations. For naturalness, we constrain the adversarial example to be $\epsilon$-isometric to the original one by adopting the Gaussian curvature as a surrogate metric guaranteed by a theoretical analysis. For invariance to physical transformations, we propose a maxima over transformation (MaxOT) method that actively searches for the most harmful transformations rather than random ones to make the generated adversarial example more robust in the physical world. Experiments on typical point cloud recognition models validate that our approach can significantly improve the attack success rate and naturalness of the generated 3D adversarial examples than the state-of-the-art attack methods.Comment: NeurIPS 202

Pseudogenization of Mc1r gene associated with transcriptional changes related to melanogensis explains leucistic phenotypes in Oreonectes cavefish (Cypriniformes, Nemacheilidae)

Organisms that have colonized underground caves encounter vastly different selective pressures than their relatives in above‐ground habitats. While disruption of certain pigmentation genes has been documented in various cave‐dwelling taxa, little is known about wider impacts across pigmentation and other gene pathways. We here study the timeframe and transcriptional landscape of a leucistic and blind cypriniform fish (Oreonectes daqikongensis, Nemacheilidae) that inhabits karst caves in Guizhou, China. Based on data from the mitochondrial ND4, ND5, and Cytb genes, we show that the divergence between O. daqikongensis and its most closely related pigmented species occurred ca. 6.82 million years ago (95% HPD, 5.12–9.01), providing ample time for widespread phenotypic change. Indeed, we found that the DNA sequence of Mc1r (melanocortin‐1 receptor), a key gene regulating the biosynthesis of melanin in most vertebrates, is pseudogenized in O. daqikongensis, caused by a 29 bp deletion in the protein‐coding region. Furthermore, 99,305 unigenes were annotated based on the transcriptome of skin tissue of Oreonectes fish. Among the differentially expressed unigenes, 7,326 (7.4% of the total unigenes) had decreased expression and 2,530 (2.5% of the total unigenes) had increased expression in O. daqikongensis skin. As predicted, the expression of Mc1r and 18 additional genes associated with melanin biosynthesis was significantly downregulated in the skin tissue of O. daqikongensis, but not in its congener. Our results, integrating with other studies on cavefishes, suggest that loss of pigmentation was caused by coding region loss‐of‐function mutations along with widespread transcriptional changes, resulting from extended evolutionary time as a cave‐dwelling form

Comparison of Segmentation Algorithms for Detecting Myocardial Infarction Using Late Gadolinium Enhancement Magnetic Resonance Imaging

Objective: The aim of this study was to validate the accuracy of a new automatic method for scar segmentation and compare its performance with that of two other frequently used segmentation algorithms. Methods: Twenty-six late gadolinium enhancement cardiovascular magnetic resonance images of diseased hearts were segmented by the full width at half maximum (FWHM) method, the n standard deviations ( n SD) method, and our new automatic method. The results of the three methods were compared with the consensus ground truth obtained by manual segmentation of the ventricular boundaries. Results: Our automatic method yielded the highest Dice score and the lowest volume difference compared with the consensus ground truth segmentation. The n SD method produced large variations in the Dice score and the volume difference. The FWHM method yielded the lowest Dice score and the greatest volume difference compared with the automatic, 6SD, and 8SD methods, but resulted in less variation when different observers segmented the images. Conclusion: The automatic method introduced in this study is highly reproducible and objective. Because it requires no manual intervention, it may be useful for processing large datasets produced in clinical applications

Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The "lost in the middle" problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community

Acoustoelectric brain imaging with different conductivities and acoustic distributions

Objective: Acoustoelectric brain imaging (AEBI) is a promising imaging method for mapping brain biological current densities with high spatiotemporal resolution. Currently, it is still challenging to achieve human AEBI with an unclear acoustoelectric (AE) signal response of medium characteristics, particularly in conductivity and acoustic distribution. This study introduces different conductivities and acoustic distributions into the AEBI experiment, and clarifies the response interaction between medium characteristics and AEBI performance to address these key challenges.Approach: AEBI with different conductivities is explored by the imaging experiment, potential measurement, and simulation on a pig’s fat, muscle, and brain tissue. AEBI with different acoustic distributions is evaluated on the imaging experiment and acoustic field measurement through a deep and surface transmitting model built on a human skullcap and pig brain tissue.Main results: The results show that conductivity is not only inversely proportional to the AE signal amplitude but also leads to a higher AEBI spatial resolution as it increases. In addition, the current source and sulcus can be located simultaneously with a strong AE signal intensity. The transcranial focal zone enlargement, pressure attenuation in the deep-transmitting model, and ultrasound echo enhancement in the surface-transmitting model cause a reduced spatial resolution, FFT-SNR, and timing correlation of AEBI. Under the comprehensive effect of conductivity and acoustics, AEBI with skull finally shows reduced imaging performance for both models compared with no-skull AEBI. On the contrary, the AE signal amplitude decreases in the deep-transmitting model and increases in the surface-transmitting model.Significance: This study reveals the response interaction between medium characteristics and AEBI performance, and makes an essential step toward developing AEBI as a practical neuroimaging technique

Transfer-free, lithography-free and fast growth of patterned CVD graphene directly on insulators by using sacrificial metal catalyst

Chemical vapor deposited graphene suffers from two problems: transfer from metal catalysts to insulators, and photoresist induced degradation during patterning. Both result in macroscopic and microscopic damages such as holes, tears, doping, and contamination, translated into property and yield dropping. We attempt to solve the problems simultaneously. A nickel thin film is evaporated on SiO2 as a sacrificial catalyst, on which surface graphene is grown. A polymer (PMMA) support is spin-coated on the graphene. During the Ni wet etching process, the etchant can permeate the polymer, making the etching efficient. The PMMA/graphene layer is fixed on the substrate by controlling the surface morphology of Ni film during the graphene growth. After etching, the graphene naturally adheres to the insulating substrate. By using this method, transfer-free, lithography-free and fast growth of graphene realized. The whole experiment has good repeatability and controllability. Compared with graphene transfer between substrates, here, no mechanical manipulation is required, leading to minimal damage. Due to the presence of Ni, the graphene quality is intrinsically better than catalyst-free growth. The Ni thickness and growth temperature are controlled to limit the number of layers of graphene. The technology can be extended to grow other two-dimensional materials with other catalysts